Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Pacheco, Bruno"'
This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in deep equilibrium models (DEQs) and physics-informed neural
Externí odkaz:
http://arxiv.org/abs/2406.03472
Semantic segmentation plays a crucial role in various computer vision applications, yet its efficacy is often hindered by the lack of high-quality labeled data. To address this challenge, a common strategy is to leverage models trained on data from d
Externí odkaz:
http://arxiv.org/abs/2402.10665
Maximizing oil production from gas-lifted oil wells entails solving Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as the basic-sediment-to-water ratio and the gas-oil ratio, are updated, the problems must be repeatedly s
Externí odkaz:
http://arxiv.org/abs/2309.00197
Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?
Autor:
Pacheco, Bruno Machado, de Oliveira, Victor Hugo Rocha, Antunes, Augusto Braga Fernandes, Pedro, Saulo Domingos de Souza, Silva, Danilo
Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estima
Externí odkaz:
http://arxiv.org/abs/2307.05241
Autor:
Pacheco, Bruno Machado, Seman, Laio Oriel, Rigo, Cezar Antonio, Camponogara, Eduardo, Bezerra, Eduardo Augusto, Coelho, Leandro dos Santos
This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbi
Externí odkaz:
http://arxiv.org/abs/2303.13773
Publikováno v:
Biomedical Signal Processing and Control, vol. 82, p. 104514, Apr. 2023
State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also kn
Externí odkaz:
http://arxiv.org/abs/2212.07497
Autor:
Camponogara, Eduardo, Seman, Laio Oriel, Müller, Eduardo Rauh, Miyatake, Luis Kin, Gaspari, Eduardo Ferreira, Vieira, Bruno Ferreira, Pacheco, Bruno Machado
Publikováno v:
In Chemical Engineering Science 5 August 2024 295
Publikováno v:
In Biomedical Signal Processing and Control April 2023 82
Publikováno v:
In Procedia CIRP 2020 93:443-448
Autor:
Pacheco, Bruno Rafael1 brunorpacheco@hotmail.com, Santos Rocha, Danilo1, Bertoncell, Dernival1
Publikováno v:
Motricidade. 2022, Vol. 18 Issue 1, p25-45. 21p.